{KgR(dZddlmZmZddlmZddlZddlm Z ddl m Z m Z m Z mZmZddlmZmZdd lmZdd lmZdd lmZdd lmZ dd ZddZGdde e eZdZGdde eeZy)z)Base class for ensemble-based estimators.)ABCMetaabstractmethod)ListN)effective_n_jobs) BaseEstimatorMetaEstimatorMixinclone is_classifier is_regressor)Bunchcheck_random_state _safe_tags)_print_elapsed_time)_routing_enabled)_BaseCompositioncts3d|vr/ t||5|j|||dddd|St||5|j||fi|ddd|S#1swY6xYw#t$rB}dt |vr/tdj |j j|d}~wwxYw#1swY|SxYw)z7Private function used to fit an estimator within a job. sample_weight)rNz+unexpected keyword argument 'sample_weight'z8Underlying estimator {} does not support sample weights.)rrfit TypeErrorstrformat __class____name__) estimatorXy fit_paramsmessage_clsnamemessageexcs Z/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/ensemble/_base.py_fit_single_estimatorr$s  /Z"? $_g> a*_2M N? !' : IMM!Q -* -; ?> <CHNUU!++44   ; s9 A5A)A5 C)A2.A55 C>=B;;CC c4t|}i}t|jdD]X}|dk(s|jds|j t j t jj||<Z|r|jdi|yy)aSet fixed random_state parameters for an estimator. Finds all parameters ending ``random_state`` and sets them to integers derived from ``random_state``. Parameters ---------- estimator : estimator supporting get/set_params Estimator with potential randomness managed by random_state parameters. random_state : int, RandomState instance or None, default=None Pseudo-random number generator to control the generation of the random integers. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. Notes ----- This does not necessarily set *all* ``random_state`` attributes that control an estimator's randomness, only those accessible through ``estimator.get_params()``. ``random_state``s not controlled include those belonging to: * cross-validation splitters * ``scipy.stats`` rvs Tdeep random_state__random_stateN) rsorted get_paramsendswithrandintnpiinfoint32max set_params)rr(to_setkeys r#_set_random_statesr6,s8&l3L Fi***56 . CLL1A$B&..rxx/A/E/EFF3K7 &v&cneZdZUdZgZeeed<e d de ddZ d dZ d dZ d Z d Zd Zy) BaseEnsembleaBase class for all ensemble classes. Warning: This class should not be used directly. Use derived classes instead. Parameters ---------- estimator : object The base estimator from which the ensemble is built. n_estimators : int, default=10 The number of estimators in the ensemble. estimator_params : list of str, default=tuple() The list of attributes to use as parameters when instantiating a new base estimator. If none are given, default parameters are used. Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. estimators_ : list of estimators The collection of fitted base estimators. _required_parametersN ) n_estimatorsestimator_paramsc.||_||_||_yN)rr<r=)selfrr<r=s r#__init__zBaseEnsemble.__init__ps#( 0r7cN|j|j|_y||_y)zMCheck the base estimator. Sets the `estimator_` attributes. N)r estimator_)r@defaults r#_validate_estimatorz BaseEnsemble._validate_estimators >> %"nnDO%DOr7c t|j}|jdi|jDcic]}|t ||c}| t |||r|j j||Scc}w)zMake and configure a copy of the `estimator_` attribute. Warning: This method should be used to properly instantiate new sub-estimators. r*)r rCr3r=getattrr6 estimators_append)r@rIr(rps r#_make_estimatorzBaseEnsemble._make_estimators{ $//*  TT=R=RS=R74#3 3=RST  # y, 7     # #I . TsA8 c,t|jS)z0Return the number of estimators in the ensemble.)lenrHr@s r#__len__zBaseEnsemble.__len__s4##$$r7c |j|S)z.Return the index'th estimator in the ensemble.)rH)r@indexs r# __getitem__zBaseEnsemble.__getitem__s&&r7c,t|jS)z0Return iterator over estimators in the ensemble.)iterrHrNs r#__iter__zBaseEnsemble.__iter__sD$$%%r7r?)TN)r __module__ __qualname____doc__r:rr__annotations__rtuplerArErKrOrRrUr*r7r#r9r9RsW6')$s)( 1 1 1 &"%'&r7r9) metaclassctt||}tj|||zt}|d||zxxxdz ccctj |}||j dg|j zfS)z;Private function used to partition estimators between jobs.)dtypeNr)minrr/fullintcumsumtolist)r<n_jobsn_estimators_per_jobstartss r#_partition_estimatorsrgs{!&)< 8F776<6+AM0<&01Q61 YY+ ,F '..01# 2G GGr7c`eZdZdZdgZedZedZdZ fdZ d fd Z dZ xZ S) _BaseHeterogeneousEnsembleaBase class for heterogeneous ensemble of learners. Parameters ---------- estimators : list of (str, estimator) tuples The ensemble of estimators to use in the ensemble. Each element of the list is defined as a tuple of string (i.e. name of the estimator) and an estimator instance. An estimator can be set to `'drop'` using `set_params`. Attributes ---------- estimators_ : list of estimators The elements of the estimators parameter, having been fitted on the training data. If an estimator has been set to `'drop'`, it will not appear in `estimators_`. estimatorsc>tdit|jS)zDictionary to access any fitted sub-estimators by name. Returns ------- :class:`~sklearn.utils.Bunch` r*)r dictrjrNs r#named_estimatorsz+_BaseHeterogeneousEnsemble.named_estimatorss-tDOO,--r7c||_yr?rj)r@rjs r#rAz#_BaseHeterogeneousEnsemble.__init__s $r7c t|jdk(r tdt|j\}}|j |t d|D}|s tdt |rt nt}|D]L}|dk7s ||rtdj|jj|jdd||fS)NrzfInvalid 'estimators' attribute, 'estimators' should be a non-empty list of (string, estimator) tuples.c3&K|] }|dk7 yw)dropNr*.0ests r# zB_BaseHeterogeneousEnsemble._validate_estimators..s@ZcC6MZszHAll estimators are dropped. At least one is required to be an estimator.rrz The estimator {} should be a {}.) rMrj ValueErrorzip_validate_namesanyr r rrr)r@namesrj has_estimatoris_estimator_typerus r#_validate_estimatorsz/_BaseHeterogeneousEnsemble._validate_estimatorss t 1 $@  1z U#@Z@@ &  .;4-@MlCf}%6s%; 6== ..0A0J0J120Nj  r7c &t|di||S)a Set the parameters of an estimator from the ensemble. Valid parameter keys can be listed with `get_params()`. Note that you can directly set the parameters of the estimators contained in `estimators`. Parameters ---------- **params : keyword arguments Specific parameters using e.g. `set_params(parameter_name=new_value)`. In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to 'drop'. Returns ------- self : object Estimator instance. ro)super _set_params)r@paramsrs r#r3z%_BaseHeterogeneousEnsemble.set_paramss, 3F3 r7c&t|d|S)a< Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the `estimators` parameter. Parameters ---------- deep : bool, default=True Setting it to True gets the various estimators and the parameters of the estimators as well. Returns ------- params : dict Parameter and estimator names mapped to their values or parameter names mapped to their values. rjr&)r _get_params)r@r'rs r#r,z%_BaseHeterogeneousEnsemble.get_paramss&w".(s6*C47q6V3C 3q6";/M*s%'F)preserves_dtyper)allrj Exception)r@rs r# _more_tagsz%_BaseHeterogeneousEnsemble._more_tags&sG ??I$&I>>  I  s # 11)T)rrVrWrXr:propertyrmrrArr3r,r __classcell__)rs@r#ririsL$)> ..%%!:2<* ?r7ri)NNr?) rXabcrrtypingrnumpyr/joblibrbaserr r r r utilsr r utils._tagsrutils._user_interfacerutils.metadata_routingrutils.metaestimatorsrr$r6r9rgrir*r7r#rsk/ (#XX-$753@D0#'LT&%}T&n H{?(G{?r7